AI Engineer

• Build and enhance AI-powered SaaS applications and internal platforms.
• Translate founder ideas, workflows, and storyboards into production-ready product features.
• Develop and maintain frontend and backend systems across multiple SaaS products.
• Integrate third-party APIs and external services.
• Develop AI-powered workflows using modern LLM technologies.

Senior AI Full Stack Product Engineer

Machine Learning Engineer

• Model Development: Assist in designing, training, and fine-tuning machine learning models using Python and modern ML frameworks.
• Data Pipeline Maintenance: Help clean, preprocess, and analyze large datasets to ensure high-quality training inputs for our models.
• Performance Evaluation: Conduct experiments to evaluate model accuracy, precision, and recall; document findings and iterate based on performance metrics.
• Research & Implementation: Stay up-to-date with the latest developments in AI/ML and explore how they can be applied to solve specific fintech problems.
• Collaborative Deployment: Work with the engineering team to integrate model prototypes into our production environment.

Machine Learning Intern

• Assisting in the design and implementation of machine learning models under the guidance of senior engineers.
• Conducting data cleaning and preprocessing to prepare datasets for training models.
• Supporting the evaluation and fine-tuning of machine learning algorithms.
• Collaborating with cross-functional teams to understand project requirements and objectives.
• Documenting processes, model training, and outcomes clearly and comprehensively.
• Staying updated with industry trends and best practices in machine learning and AI.
• Participating in team meetings and contributing to brainstorming sessions.

Junior Machine Learning Engineer

• Independently design and implement CI/CD pipelines for machine learning models.
• Automate the process of model training, validation, testing, and deployment using relevant tools.
• Develop and maintain automated testing frameworks for machine learning models.
• Develop and manage various model deployment strategies (e.g., A/B testing, canary deployments).
• Build and maintain scalable and reliable infrastructure for model serving (e.g., using Kubernetes, serverless functions).
• Implement and manage model versioning and rollback mechanisms.
• Optimize model serving for latency, throughput, and resource utilization.
• Implement and manage comprehensive monitoring and logging systems to track model performance and identify issues (e.g., model drift, data drift).
• Set up and manage alerting systems to notify the team of performance degradation.
• Contribute to the development and implementation of model governance policies and procedures.
• Ensure compliance with security and privacy requirements.
• Collaborate effectively with data scientists to understand model requirements and dependencies.
• Work with software engineers to integrate machine learning models into applications and services.
• Develop and maintain APIs and interfaces for model access.

MLOps Engineer II

• Owning the design and implementation of the AI-driven customer care systems and autonomous multi-agent orchestration workflows.
• Designing, developing, and scaling state-of-the-art cyclic graph agent networks and multi-agent systems using frameworks like LangGraph, CrewAI, or AutoGen.
• Optimizing LLM & Agent execution utilizing advanced runtime techniques such as quantization, pruning, batching, token streaming, and semantic caching to ensure ultra-low latency.
• Owning the solutions alignment of dependencies and service contracts with other teams.
• Designing, developing, and scaling real-time Retrieval-Augmented Generation (RAG) pipelines integrating state-of-the-art open-source LLMs (Llama 3, Mistral, Falcon, or similar).
• Implementing scalable, high-performance vector search (Qdrant, Weaviate, Milvus) for robust knowledge retrieval and semantic search.
• Having awareness of techniques such as quantization, pruning, distillation, batching, and caching for optimizing LLM inference with the minimum response times.
• Developing and exposing secure, performant APIs via FastAPI/gRPC or others, containerized (Docker), orchestrated (Kubernetes), and fully integrated into automated CI/CD pipelines.
• Embedding comprehensive monitoring and evaluation (e.g. MRR, Recall@k, NDCG, Faithfulness, latency metrics) and implementing automated regression testing for continuous improvement.
• Championing and enforcing best practices for data security, compliance (GDPR, Saudi PDPL is a plus), and responsible AI, including PII redaction and end-to-end encryption.
• Demonstrating mastery of foundational software engineering by writing clean code and architecture, maintainable and testable code, designing robust, modular, and scalable systems; leveraging version control, and implementing comprehensive continuous integration, automated testing, and deployment practices.
• Leading rigorous design and code reviews, mentoring engineers, and fostering an innovative engineering culture grounded in clean architecture, SOLID principles, and proactive best practices to ensure system reliability, security, and agility.

AI Engineering Lead

• Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment:
• Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines.
• Define standards for data modeling, storage, ingestion, and transformation across client engagements.
• Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring).
• Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling.
• Define best practices and reusable frameworks for data engineers, analysts, and data scientists.
• Act as a technical mentor and reviewer for cross-functional project teams.
• Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects.
• Contribute to internal knowledge base, toolkits, and delivery accelerators.
• Lead architecture workshops and discovery sessions with client stakeholders.
• Translate business requirements into scalable, robust technical blueprints.
• Present architecture decisions to both technical teams and executive audiences.
• Support pre-sales and proposal efforts with technical scoping and solution design.
• Provide internal training and knowledge-sharing sessions with the team.
• Support the Head of Practice on business development and internal capability initiatives.

Analytics & AI Architect

• Implement new AI features in products
• Build tools & frameworks augmenting the capabilities of LLM/VLMs
• Build AI automations & AI agents
• Fine-tune AI models
• Fine-tune and maintain models with the HuggingFace toolkit
• Monitor model's performance
• Work with and guide internal teams on building custom datasets
• Rapidly build and test AI-driven concepts — moving from proof of concept → MVP → production.
• Evaluate existing solutions (both internal & external) and conduct experiments on potential improvements

• Build autonomous agents using frameworks like LangChain, LangGraph, AutoGen, and CrewAI that can plan, reason, and execute tasks iteratively.
• Integrate and fine-tune LLMs (e.g., GPT, Claude, LLaMA) for use in agent workflows, including prompt engineering and retrieval-augmented generation (RAG).
• Develop systems where multiple agents collaborate, delegate tasks, and adapt strategies based on real-time feedback.
• Enable agents to interact with external systems (e.g., APIs, databases, web apps) to perform actions autonomously.
• Use tools like LangSmith, Grafana, and Splunk to monitor agent behavior, performance, and reliability.
• Work closely with data scientists, architects, and product teams to align agentic solutions with business goals.
• Ensure all solutions are well-documented and comply with data privacy and security standards.

Agentic AI Developer

• Lead and oversee the AI team in designing, developing, and deploying AI and machine learning models and solutions.
• Define and execute the AI strategy aligned with business goals and emerging industry trends.
• Collaborate with cross-functional teams including data science, engineering, product management, and business units to identify AI-driven opportunities and deliver impactful solutions.
• Manage AI projects ensuring timely delivery, quality, and alignment with organizational objectives.
• Mentor and develop the AI team by fostering skills growth, collaboration, and innovative thinking.
• Stay abreast of the latest AI research, tools, and methodologies and assess their applicability to business problems.
• Communicate complex AI concepts clearly to non-technical stakeholders to facilitate informed decision-making.
• Advocate best practices for AI ethics, data privacy, and security within the organization.

AI Lead

• Design, develop, and implement artificial intelligence solutions.
• Work closely with data scientists, engineers, and stakeholders.
• Build scalable AI models and integrate them into product offerings.
• Involved in all stages of AI project development from data collection to model monitoring.

Machine Learning Engineer jobs in Egypt

See all jobs on JobTailor

Senior AI Full Stack Product Engineer

Machine Learning Intern

Junior Machine Learning Engineer

MLOps Engineer II

AI Engineering Lead

Analytics & AI Architect

AI Engineer

Agentic AI Developer

AI Lead

AI Engineer